Winston H. Wu, Maxim A. Batalin, Lawrence K. Au, Alex A. T. Bui, and William J. Kaiser

 Purpose-Low power consuming physiological sensors implementation  Energy use decreased by enabling & disabling the sensors to real time measurement demand  Use low cost sensors to schedule high cost sensors like ECG sensors

 Commercially Available PDA with Wifi capabilities  Bluetooth modules  3 Sensors o ECG sensor o Pulse Oximeter o 3 Axis Accelerometer-2 sets

 Inference Engine  GUI  Local Data Logger  Device Server  Device Driver

Software Arcitecture

Motion detection  Pulse oximeter used to detect start of the exercise  2 Accelerometers used to detect end of the exercise  1 on right ankle and 1 on left hip  Inference engine on the wearable system computes when to activate ECG sensor  Data collected is streamed to a central server via Wifi Network

Communication Via Bluetooth  Each data point accompanied by tracking sequence number to check for errors  PDA is the master node over bluetooth network

 Feature Extraction  Pulse rate and SpO2 value-rate of decline of oxygen saturation  Accelerometer Since cyclical movements are involved Features from spectral domain are used In general case features from time domain may be used 512 data points window-100 points entered every second 2 spectral feature values extracted from each axis -f peak and f energy

Context Aware Sensing Algorithm of an ECG signal

 P(C/F)  Where C is the patient states of interest  F is the feature vector  Pulse classification as Low, Medium, High  When high Accelerometer activated  Accelerometer classifies as Rest, Walk, Jog, Run  If Jog or Run ECG sensor not activated  Else it is activated